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Pork barrel scam: Visualizing the ties that bind

The release of the Commission on Audit (COA) Special Audits Office Report No. 2012-03 [1] opened a wealth of data that gives us a glimpse of how the Priority Development Assistance Fund (PDAF) and Various Infrastructure, including local projects (VILP) were used in government agencies. The report gives a government-wide Performance Audit of the PDAF and the VILP of various implementing agencies from 2007 to 2009.

The data is in several tables and annexes that listed the non-governmental organizations (NGO) to whom funding was given by a legislator. One cannot immediately see the relationships between the NGOs and between legislators by simply looking at the tables. Visualizing such relationships and quantifying them is a problem of network visualization. This visualization and analysis is what we address in this technical note.

Figure: Legislator network and detected communities. Sizes of the nodes are proportional to the degree or number of connections.

We present this visualization at http://visser.ph/pdaf

Network analysis

Network tools have been used in many applications to date. We have analyzed different systems such as prose and poetry [2], SMS messages [3], translations [4], poetic styles [5] and bill co-authorships in the Philippine Congress[6] among others.

A network is a simple way to represent a set of objects or nodes who has a defined relation between each other. We call these objects a node or a vertex while we call the relationship between them as an edge [7]. Depending on the data set, edges could represent different kinds of relationships. In a social network, these could be friendship relations [8], or co-authorships in a Congressional setting[9]. Edges in networks can have values attached to them (weighted networks) or one can set a uniform weight of one for unweighted networks [7]. Depending also on the direction at which the relationship is defined we can have directed or undirected networks.

Networks have been used to characterized political systems such as the United States Congress [10] and the Philippine House of Representatives[6]. In such networks, nodes (called ego or actors) are the legislators themselves and links between them are either voting patterns or co-sponsorships of bills and resolutions[6]. These co-sponsorship networks can be used as proxies for effective political party affiliation of the legislators which can be derived from calculating partitions, or communities, that arise as a result of their level of partisanship[10].

In general, these nodes or actors can be persons, groups or organizations. In the current work, we have our actors as the individual senators and congressmen as well as the NGOs that received their PDAF allocations.

Methdology

We took data from Annex A of the COA special report[1] and converted this to a table that can be processed in an open source network analysis program Gephi[11]. We have taken two visualizations of the data as presented by looking at the network built from legislators who have released funds to a common NGO and another network where NGOs who received funds from the same legislator is linked together. The two networks taken together defines a bipartite network but we only present visualization and analysis of the two networks taken separately.

The first network that we created is a network where a legislator, represented by a node, is connected to another legislator if the two of them provided funds to the same NGO. We applied community detection algorithms in Gephi[11] to determine if there were groups of legislators that were likely to fund NGOs together. The legislators were colored based on what “community” they fell into. This allows us to quickly visualize these communities.

Figure: NGO networks and detected communities

The same was done to make a network of NGOs to which the PDAF were transferred. In this network, a connection is made between two NGOs if they received funds from the same legislator. As these nodes tend to connect more with certain sets of nodes, we use the same community detection methods to find what NGOs are connected with each other more and group them together by color. As with the legislator networks, the colors are based on the communities that the NGOs fall into.

Results and Analysis

The legislator network that we obtained has 186 nodes with 3976 edges. We find that the network has an average degree of 21.38. Degree is the number of connections that a node has. This implies that, on the average, a legislator distributes his PDAF to 21.38 NGOs. We show in Figure 1 the legislator network and their detected communities. We found six (6) communities. These communities are legislators that tend to give to the same set of NGOs together.

We can rank the legislators based the number of “partners” they have, of which Adam Relson Jala tops the list. This is reflected in the high degree or number of connections that he has. We can also add weight to these links by the number of times that two linked legislators funded a common NGO. In such a weighted network, Nerissa Corazon-Ruiz becomes the most connected legislator. A more advanced technique is betweenness centrality which measures how “central” a given node is, which can be seen as a proxy for influence within the network[10]. Sen. Juan Ponce Enrile tops that list. See Table 1 for more details.

On the other hand, the NGO network shows an average degree of 7.68 which implies that NGOs typically receive allocations from 7.68 different legislators on the average. The NGO network has 69 nodes with 530 edges. We show in Figure 2 the NGO network and the detected communities. We found five (5) groups of nodes or communities. Each groups are represented by a separate node color. As such, nodes with the same color are NGOs which are more likely to have receive funds from the same legislator.

In figure 2, the thickness of the arrows represents the weight of how the NGOs are connected with each other. The stronger the weight of the arrows, the greater their connection is. Thicker arrows are funded together more often by more than one legislator. It can be seen that the nodes in blue and green groups have more weighted edges than the other groups especially in the SDPFFI NGO in the blue group and KKAMFI NGO in the green group.

We could also apply some other network techniques such as looking at which node are important nodes in the NGO network. This is measured by the eigenvector centrality[11]. The NGO which has the highest eigenvector centrality or the measure of importance of the node is the MAMFI NGO. This is followed by the CARED and SDPFF respectively with all of which have measure of greater than 0.90 eigenvector centrality. See Table 2 for more numbers.

Node label

Betweenness Centrality

Weighted Degree

Clustering Coefficient

Eigenvector Centrality

Closeness Centrality

1

Juan Ponce Enrile

0.1034

140.0

0.2661

0.26504

0.5378

2

Arrel R. Olano

0.1024

87.0

0.3529

0.70891

0.5640

3

Ignacio T. Arroyo, Jr.

0.0750

95.0

0.5340

0.74083

0.5441

4

Adam Relson L. Jala

0.0650

110.0

0.3916

1.00000

0.5763

5

Francisco T. Matugas

0.0649

78.0

0.4713

0.77308

0.5425

6

Edgardo J. Angara

0.0532

12.0

0.2778

0.03684

0.4048

7

Mariano U. Piamonte

0.0403

106.0

0.4638

0.95405

0.5378

8

Emmanuel Joel J. Villanueva

0.0402

50.0

0.4366

0.43666

0.5082

9

Samuel M. Dangwa

0.0382

92.0

0.3286

0.26147

0.4973

10

Marc Douglas C. Cagas IV

0.0356

66.0

0.4473

0.46821

0.5211

Table 1. Various network parameters in the legislator network for the top-10 legislators based on their betweeness centrality.

Betweenness centrality is defined as the number of shortest paths from all vertices passing through a node. The weighted degree is proportional to the number and strength of connections of a node. The clustering coefficient measures the degree to which nodes tend to cluster with one another. The eignevector centrality is a measure of the importance of a node in the network and is related to how well connected a particular node is. The closeness centrality is a measure of the average distance from a given starting node to all other nodes in the system. The centrality measures are normalized in this table.

These are just a sample of the things we can do with the network representation of the PDAF releases. Deeper knowledge about Congress and the interlocking directorships of the NGOs would be also be extremely helpful in further analysis. This will be done in a future work. Nevertheless these tools allow the average Filipino to glean information readily as opposed to tables and documents, helping them better participate in the process of democracy.

Node label

Betweenness Centrality

Weighted Degree

Clustering Coefficient

Eigenvector Centrality

Closeness Centrality

1

Kagandahan ng Kapaligiran Foundation, Inc. (KKFI)

0.1982

21.0

0.3429

0.8683

0.5397

2

Kabuhayan at Kalusugan Alay sa Masa Foundation, Inc. (KKAMFI)

0.1823

23.0

0.2332

0.6561

0.5574

3

Dr. Rodlofo A. Ignacio, Sr. Foundation Inc (DRAISFI)

0.1275

23.0

0.3360

1.0

0.5667

4

Farmerbusiness Development Corp (FDC)

0.0984

18.0

0.3399

0.7372

0.5231

5

Aaron Foundation Philippines Inc (AFPI)

0.0915

15.0

0.2380

0.5067

0.4892

6

Masaganang Ani Para sa Magsasaka Foundation Inc (MAMFI)

0.0903

21.0

0.4048

0.8651

0.5397

7

Pangkabuhayan Foundation (Pang-FI)

0.0838

21.0

0.3524

0.8509

0.4963

8

Kaagapay Magpakailanman Foundation Inc (KMFI)

0.0681

17.0

0.3088

0.6091

0.5312

9

ITO NA Movement Foundation Inc (ITO NA MI)

0.0571

11.0

0.3818

0.4734

0.4755

10

Hand-Made Living Foundation Inc (HMLFI)

0.0451

10.0

0.3556

0.4486

0.4626

Table 2. Various network parameters in the NGO network for the top-10 NGOs based on their betweeness centrality.

Betweenness centrality is defined as the number of shortest paths from all vertices passing through a node. The weighted degree is proportional to the number and strength of connections of a node. The clustering coefficient measures the degree to which nodes tend to cluster with one another. The eignevector centrality is a measure of the importance of a node in the network and is related to how well connected a particular node is. The closeness centrality is a measure of the average distance from a given strarting node to all other nodes in the system. The centrality measures are normalized in this table.

11. Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.

Pamela Anne Pasion is a fourth-year BS Applied Physics student at the National Institute of Physics working on translations and network analysis. Gabriel Dominik Sison has finished his BS Physics degree in 2013 with an award for Best BS Thesis on his work “Edge-weight distributions in dense small node co-authorship networks“. Dr. Giovanni Tapang is an Associate Professor at the National Institute of Physics and is also the chairperson of the scientist group AGHAM-Advocates of Science and Technology for the People. He can be reached at gtapang@nip.upd.edu.ph

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Welcome to Rappler, a social news network where stories inspire community engagement and digitally fuelled actions for social change. Rappler comes from the root words "rap" (to discuss) + "ripple" (to make waves).